def after_training_exp(self, strategy, **kwargs):
"""
Save a copy of the model after each experience
and freeze the prev model and freeze the last layer of current model
"""
self.prev_model = copy.deepcopy(strategy.model)
freeze_everything(self.prev_model)
last_fc_name, last_fc = get_last_fc_layer(strategy.model)
for param in last_fc.parameters():
param.requires_grad = False
def freeze_other_layers(self):
cwr_layer = self.get_cwr_layer()
if cwr_layer is None:
raise RuntimeError('Can\'t find a the Linear layer')
freeze_everything(self.model)
unfreeze_everything(cwr_layer)